library(tidyverse)
library(ggsignif)
library(broom)
library(ComplexHeatmap)
library(RColorBrewer)
library(heatmaply)
library(factoextra)
library(plotly)
library(GGally)
# devtools::install_github('vqv/ggbiplot')
library(ggbiplot)
library(corrplot)

source(here::here("Functions", "Functions.R"))

GGscatterPlot <- function(data, mapping, ..., method = "spearman") {
    
    # Get correlation coefficient
    x <- GGally::eval_data_col(data, mapping$x)
    y <- GGally::eval_data_col(data, mapping$y)
    
    cor <- cor(x, y, method = method)
    # Assemble data frame
    df <- data.frame(x = x, y = y)
    # PCA
    nonNull <- x != 0 & y != 0
    dfpc <- prcomp(~x + y, df[nonNull, ])
    df$cols <- predict(dfpc, df)[, 1]
    # Define the direction of color range based on PC1 orientation:
    dfsum <- x + y
    colDirection <- ifelse(dfsum[which.max(df$cols)] < dfsum[which.min(df$cols)], 
        1, -1)
    # Get 2D density for alpha
    dens2D <- MASS::kde2d(df$x, df$y)
    df$density <- fields::interp.surface(dens2D, df[, c("x", "y")])
    
    if (any(df$density == 0)) {
        mini2D = min(df$density[df$density != 0])  #smallest non zero value
        df$density[df$density == 0] <- mini2D
    }
    # Prepare plot
    pp <- ggplot(df, aes(x = x, y = y, color = cols, alpha = 1/density)) + ggplot2::geom_point(shape = 16, 
        show.legend = FALSE) + ggplot2::scale_color_viridis_c(direction = colDirection) + 
        # scale_color_gradient(low = '#0091ff', high = '#f0650e') +
    ggplot2::scale_alpha(range = c(0.05, 0.6)) + ggplot2::geom_abline(intercept = 0, 
        slope = 1, col = "darkred") + ggplot2::geom_label(data = data.frame(xlabel = min(x, 
        na.rm = TRUE), ylabel = max(y, na.rm = TRUE), lab = round(cor, digits = 3)), 
        mapping = ggplot2::aes(x = xlabel, y = ylabel, label = lab), hjust = 0, vjust = 1, 
        size = 3, fontface = "bold", inherit.aes = FALSE  # do not inherit anything from the ...
) + 
        theme_minimal()
    
    return(pp)
}

## Load Gabriels data table
data_Gab <- read_csv(here::here("Data", "Lifespan_Study_Data_updated.csv")) %>% filter(Treatment %in% 
    c("Control_21", "Control_3")) %>% filter(Cell_Line %in% c("hFB11", "hFB12", "hFB13")) %>% 
    filter(Date %in% c("03/07/2019", "03/21/2019", "04/04/2019", "04/18/2019")) %>% 
    mutate(Date_of_Experiment = case_when(Date == "03/07/2019" ~ "2019.03.08", Date == 
        "03/21/2019" ~ "2019.03.22", Date == "04/04/2019" ~ "2019.04.05", Date == 
        "04/18/2019" ~ "2019.04.19")) %>% dplyr::rename(Treatment_Oxygen = Treatment) %>% 
    mutate(Treatment = case_when(Treatments == "Control" ~ "Ctrl")) %>% select(-Treatments) %>% 
    droplevels()

## Remove columns that only contain NAs
data_Gab <- data_Gab %>% select_if(~!sum(is.na(.)) >= nrow(data_Gab))

## Load the Seahorse data
data_Seahorse <- list.files(here::here("Data"), pattern = "*Final.csv", full.names = T) %>% 
    lapply(read_csv) %>% bind_rows %>% separate(X1, into = c("Cell_Line", "Percent_Oxygen", 
    "Treatment")) %>% select(-contains("PPR")) %>% filter(Treatment %in% "Ctrl") %>% 
    droplevels()
data_Seahorse$Percent_Oxygen <- as.numeric(data_Seahorse$Percent_Oxygen)

## Finally combine both datasets
data_combined <- full_join(data_Gab, data_Seahorse, by = c("Cell_Line", "Date_of_Experiment", 
    "Percent_Oxygen", "Treatment")) %>% unite(Group, Cell_Line, Percent_Oxygen, remove = FALSE)

## Remove the percent sign from Telomere_Length_CV
data_combined$Telomere_Length_CV <- as.numeric(str_remove(data_combined$Telomere_Length_CV, 
    "%"))
data_combined$Cell_Line <- as.factor(data_combined$Cell_Line)
data_combined$Percent_Oxygen <- as.factor(data_combined$Percent_Oxygen)

test <- data_combined %>% select(Cell_Line, Percent_Oxygen, Days_Grown, Passage, 
    Notes)
test[which(!test$Notes == "NA"), ]

data_combined <- data_combined %>% filter(!c(Cell_Line == "hFB11" & Passage == "15")) %>% 
    select(-Notes)

## Remove columns that only contain NAs
data_combined <- data_combined %>% select_if(~!sum(is.na(.)) >= nrow(data_combined) - 
    1) %>% select(-contains("RNAseq"))

InDependent_vars <- c("Sample", "Cell_Line_Group", "Unique_Variable_Name", "Group", 
    "Cell_Line", "Cell_Type", "Sex", "Clinical_Condition", "Study_Part", "Replicate_Line", 
    "Treatment", "Treatment_Oxygen", "Percent_Oxygen", "Date", "Time", "Date_1", 
    "pre_study_doublings", "Cells_Plated", "Cells_Counted", "Days_Per_Passage", "basename", 
    "Date_of_Experiment", "Experiment_Name", "mitotic_age_per_day", "mitotic_age_per_doubling", 
    "Donor_Age", "Donor_Age_with_gestation", "Passage", "Days_Grown", "Intial_Mitotic_Age", 
    "pre_study_days_grown", "pre_study_doublings")

Dependent_vars <- colnames(data_combined)[!colnames(data_combined) %in% c(InDependent_vars)]
dta <- qpcR:::cbind.na(InDependent_vars, Dependent_vars)
dta[is.na(dta)] <- " "
knitr::kable(dta, caption = "")

Age_time_vars <- c("MiAge_Population_Doublings", "MiAge_Days_Grown", "Telomere_Length", 
    "Telomere_Length_CV", "PanTissue_Clock", "PanTissue_Normalized", "Hannum_Clock", 
    "Skin_Blood_Clock", "PhenoAge_Clock", "GrimAge_Clock", "GrimAge_Normalized", 
    "DNAmTL", "Mitotic_Age", "DunedinPoAm_Age")
Seahorse_vars <- c("Baseline_Respiration", "NonMitochondrial_Respiration", "Basal_Respiration", 
    "Max_Respiration", "Spare_Respiration", "Proton_Leak", "ATPlinked_Respiration", 
    "Coupling_Efficiency", "Baseline_ECAR", "Max_ECAR", "Spare_ECAR", "ATPtotal", 
    "ATPglyc", "ATPox", "ATPtotal_max", "ATPglyc_max", "ATPox_max", "ATPtotal_spare", 
    "ATPox_spare", "ATPglyc_spare", "Resting_Metabolic_Rate", "Max_Metabolic_Rate", 
    "PicoWatts", "Max_PicoWatts", "OCRcoupled")
Glyc_vars <- c("NonMitochondrial_Respiration", "Baseline_ECAR", "Max_ECAR", "Spare_ECAR", 
    "ATPglyc", "ATPtotal_max", "ATPglyc_max", "ATPglyc_spare", "ATPtotal_max", "ATPglyc_max", 
    "ATPox_max", "ATPtotal_spare", "ATPox_spare", "ATPglyc_spare")
Mito_vars <- c("OCRcoupled", "ATPox_max", "ATPox_spare", "ATPox", "Proton_Leak", 
    "Basal_Respiration", "Max_Respiration", "Spare_Respiration", "ATPlinked_Respiration", 
    "Coupling_Efficiency", "Baseline_Respiration", "Copy_Number", "cf_mtDNA", "cf_nDNA")
ATP_vars <- c("ATPlinked_Respiration", "ATPtotal", "ATPglyc", "ATPox")
Doubling_vars <- c("Divisions_Per_Passage", "Population_Doublings", "Population_Doubling_Time", 
    "Doubling_Rate")
Cell_vars <- c("Cell_Size", "Cell_Volume", "Percent_Dead", "Divisions_Per_Passage", 
    "Population_Doublings", "Population_Doubling_Time", "Doubling_Rate")
Immune_vars <- c("IL6", "IL6_Normalized", "cf_mtDNA", "cf_nDNA")

dta <- qpcR:::cbind.na(Age_time_vars, Seahorse_vars, Glyc_vars, Mito_vars, ATP_vars, 
    Doubling_vars, Cell_vars, Immune_vars)
dta[is.na(dta)] <- " "
knitr::kable(dta, caption = "")

data_long <- data_combined %>% pivot_longer(cols = paste(Dependent_vars), names_to = "feature", 
    values_to = "value")

data_FC <- data_long %>% filter(Percent_Oxygen %in% "21") %>% dplyr::rename(baseline = value) %>% 
    select(-c("Sample", "Cell_Line_Group", "Unique_Variable_Name", "Group", "Cell_Type", 
        "Sex", "Clinical_Condition", "Donor_Age", "Donor_Age_with_gestation", "Study_Part", 
        "Replicate_Line", "Percent_Oxygen", "Date", "Time", "Date_1", "Treatment_Oxygen", 
        "mitotic_age_per_day", "mitotic_age_per_doubling", "Intial_Mitotic_Age", 
        "pre_study_days_grown", "pre_study_doublings", "Cells_Plated", "Cells_Counted", 
        "Days_Per_Passage", "basename", "Experiment_Name")) %>% full_join(data_long, 
    by = c("Cell_Line", "Days_Grown", "Treatment", "Passage", "Date_of_Experiment", 
        "feature")) %>% mutate(Normalized = (value/baseline) * 100) %>% select(-c(baseline, 
    value)) %>% pivot_wider(names_from = "feature", values_from = "Normalized")
data_FC <- na_if(data_FC, Inf)
data_FC <- na_if(data_FC, -Inf)

data_long <- data_combined %>% pivot_longer(cols = paste(Dependent_vars), names_to = "feature", 
    values_to = "value")  #%>%
# filter(Cell_Line %in% 'hFB12') %>% filter(Date_of_Experiment %in% '2019.04.19'
# ) %>% filter(feature %in% 'Telomere_Length')

data_logFC <- data_long %>% filter(Percent_Oxygen %in% "21") %>% dplyr::rename(baseline = value) %>% 
    select(-c("Sample", "Cell_Line_Group", "Unique_Variable_Name", "Group", "Cell_Type", 
        "Sex", "Clinical_Condition", "Donor_Age", "Donor_Age_with_gestation", "Study_Part", 
        "Replicate_Line", "Treatment_Oxygen", "Date", "Time", "Percent_Oxygen", "Date_1", 
        "mitotic_age_per_day", "mitotic_age_per_doubling", "Intial_Mitotic_Age", 
        "pre_study_days_grown", "pre_study_doublings", "Cells_Plated", "Cells_Counted", 
        "Days_Per_Passage", "basename", "Experiment_Name", "Days_Grown")) %>% full_join(data_long, 
    by = c("Cell_Line", "Treatment", "Passage", "Date_of_Experiment", "feature")) %>% 
    mutate(Normalized = log2(value/baseline)) %>% select(-c(baseline, value)) %>% 
    pivot_wider(names_from = "feature", values_from = "Normalized")
data_logFC <- na_if(data_logFC, Inf)
data_logFC <- na_if(data_logFC, -Inf)

Var_choice <- all_of(Dependent_vars)
data_combined %>% timeline_plot()

data_FC %>% timeline_plot()

nums <- unlist(lapply(data_combined, is.numeric))
nums_vars <- colnames(data_combined[, nums])
nums_vars <- nums_vars[!nums_vars %in% c("Sample", "Study_Part")]
print(nums_vars)

data_normoxia <- data_combined %>% filter(Percent_Oxygen %in% "21")
numdata_normoxia <- data_normoxia[, c(paste(nums_vars))] %>% select(-c("mitotic_age_per_day", 
    "mitotic_age_per_doubling", "Replicate_Line"))

data_hypoxia <- data_combined %>% filter(Percent_Oxygen %in% "3")
numdata_hypoxia <- data_hypoxia[, c(paste(nums_vars))] %>% select(-c("mitotic_age_per_day", 
    "mitotic_age_per_doubling", "Replicate_Line"))

set.seed(123)

data.scaled <- scale(numdata_normoxia[, Dependent_vars])
# Optimal number of clusters in the data

### Elbow method (look at the knee) Elbow method for kmeans
factoextra::fviz_nbclust(data.scaled, kmeans, method = "wss") + geom_vline(xintercept = 2, 
    linetype = 2) + labs(title = "Elbow method for kmeans Normoxia data")

# Average silhouette for kmeans
factoextra::fviz_nbclust(data.scaled, kmeans, method = "silhouette") + labs(title = "Average silhouette for kmeans Normoxia data")

### Gap statistic
library(cluster)
set.seed(123)
# Compute gap statistic for kmeans
gap_stat <- clusGap(data.scaled, FUN = kmeans, nstart = 25, K.max = 10, B = 500)
print(gap_stat, method = "firstmax")

factoextra::fviz_gap_stat(gap_stat) + labs(title = "Gap statistics for kmeans Normoxia data")

# Gap statistic for hierarchical clustering
gap_stat <- clusGap(data.scaled, FUN = hcut, K.max = 10, B = 10)
fviz_gap_stat(gap_stat) + labs(title = "Gap statistics for hclust Normoxia data")

# }

set.seed(123)

data.scaled <- scale(numdata_hypoxia[, Dependent_vars])
# Optimal number of clusters in the data

### Elbow method (look at the knee) Elbow method for kmeans
factoextra::fviz_nbclust(data.scaled, kmeans, method = "wss") + geom_vline(xintercept = 2, 
    linetype = 2) + labs(title = "Elbow method for kmeans Hypoxia data")

# Average silhouette for kmeans
factoextra::fviz_nbclust(data.scaled, kmeans, method = "silhouette") + labs(title = "Average silhouette for kmeans Hypoxia data")

### Gap statistic
library(cluster)
set.seed(123)
# Compute gap statistic for kmeans
gap_stat <- clusGap(data.scaled, FUN = kmeans, nstart = 25, K.max = 10, B = 500)
print(gap_stat, method = "firstmax")

factoextra::fviz_gap_stat(gap_stat) + labs(title = "Gap statistics for kmeans Hypoxia data")

# Gap statistic for hierarchical clustering
gap_stat <- clusGap(data.scaled, FUN = hcut, K.max = 10, B = 10)
fviz_gap_stat(gap_stat) + labs(title = "Gap statistics for hclust Hypoxia data")

# }

dataHM <- data_combined %>% 
  unite(heatmapgp, Cell_Line, Percent_Oxygen,  Days_Grown, sep="_", remove=F) %>%
  column_to_rownames("heatmapgp")

dataHM <- dataHM[ c("hFB11_21_20", "hFB11_21_34", "hFB11_21_48",
                        "hFB11_3_20", "hFB11_3_34", "hFB11_3_48",
                        "hFB12_21_20", "hFB12_21_34", "hFB12_21_48", "hFB12_21_62", 
                        "hFB12_3_20",  "hFB12_3_34",  "hFB12_3_48",  "hFB12_3_63",
                        "hFB13_21_21", "hFB13_21_35","hFB13_21_49","hFB13_21_63", 
                        "hFB13_3_21",  "hFB13_3_35", "hFB13_3_49",  "hFB13_3_63" 
                        ),]
dataHM$Percent_Oxygen <- as.character(dataHM$Percent_Oxygen)

#meta_data <- dataHM[,colnames(dataHM) %in% meta_names]
exprs <- dataHM[,colnames(dataHM) %in% Dependent_vars]
exprs <- t(scale(exprs))


column_ha <- HeatmapAnnotation(Donor = anno_block(gp = gpar(1:3), labels = c("Donor1", "Donor2", "Donor3")),
                               Days_grown = anno_barplot(as.numeric(dataHM$Days_Grown)),
                               Oxygen = dataHM$Percent_Oxygen,
                               col=list(Oxygen =  c("21"= "steelblue3", "3"= "mistyrose3")))

ht <- Heatmap(exprs,
              col=colorRampPalette(rev(c('#a50026','#d73027','#f46d43','#fdae61','#fee090','white','#e0f3f8','#abd9e9','#74add1','#4575b4','#313695')))(256),
              #col=colorRampPalette(rev(brewer.pal(20, "RdYlBu")))(256),
              top_annotation = column_ha,
              show_column_names=F,
              column_split = factor(dataHM$Cell_Line, levels = unique(dataHM$Cell_Line)),
              row_names_gp = gpar(col = "black",fontsize = 6),
              cluster_columns=FALSE)
    
ht = draw(ht)

dataHM <- data_logFC %>% 
  unite(heatmapgp, Cell_Line, Percent_Oxygen,  Days_Grown, sep="_", remove=F) %>%
  column_to_rownames("heatmapgp") %>%
  filter(Percent_Oxygen %in% "3")

dataHM <- dataHM[ c("hFB11_3_20", "hFB11_3_34", "hFB11_3_48",
                    "hFB12_3_20",  "hFB12_3_34",  "hFB12_3_48",  "hFB12_3_63",
                    "hFB13_3_21",  "hFB13_3_35", "hFB13_3_49",  "hFB13_3_63" 
                        ),]
dataHM$Percent_Oxygen <- as.character(dataHM$Percent_Oxygen)

#meta_data <- dataHM[,colnames(dataHM) %in% meta_names]
exprs <- dataHM[,colnames(dataHM) %in%  Dependent_vars]
exprs <- t(exprs)


column_ha <- HeatmapAnnotation(Donor = anno_block(gp = gpar(1:3), labels = c("Donor1", "Donor2", "Donor3")),
                               Days_grown = anno_barplot(as.numeric(dataHM$Days_Grown))
                               )

ht <- Heatmap(exprs,
              col=colorRampPalette(rev(c('#a50026','#d73027','#f46d43','#fdae61','#fee090','white','#e0f3f8','#abd9e9','#74add1','#4575b4','#313695')))(256),
              #col=colorRampPalette(rev(brewer.pal(20, "RdYlBu")))(256),
              top_annotation = column_ha,
              show_column_names=F,
              column_split = factor(dataHM$Cell_Line, levels = unique(dataHM$Cell_Line)),
              row_names_gp = gpar(col = "black",fontsize = 6),
              cluster_columns=FALSE)
    
ht = draw(ht)

Higher_in_hypoxia <- c("cf_nDNA", "ATPglyc", "Baseline_ECAR", "NonMitochondrial_Respiration", 
    "IL6_Normalized", "IL6", "cf_mtDNA")
Lower_in_hypoxia <- c("ATPtotal_spare", "Basal_Respiration", "ATPox", "ATPlinked_Respiration", 
    "OCRcoupled", "Max_Metabolic_Rate", "Max_Respiration", "ATPox_max", "Spare_Respiration", 
    "ATPox_spare", "Copy_Number", "Telomere_Length_CV")

dta <- qpcR:::cbind.na(Higher_in_hypoxia, Lower_in_hypoxia)
dta[is.na(dta)] <- " "
knitr::kable(dta, caption = "")

data_combined %>%
  select(Percent_Oxygen, paste(Lower_in_hypoxia))%>%#,  paste(Higher_in_hypoxia)) %>% 
  ggpairs(., mapping = ggplot2::aes(color=Percent_Oxygen), 
          lower = list(continuous = wrap("smooth",  alpha = 0.3, size=0.1)),
          upper = list(continuous = wrap("cor", size = 2))) +
  theme_bw() +
  theme(strip.placement = "outside", 
        text = element_text(size = 1),
        strip.text.x = element_text(size = 3),
        strip.text.y = element_text(size = 2))

# p<-   ggpairs(d, mapping = ggplot2::aes(color=Percent_Oxygen), 
#           lower = list(continuous = wrap("smooth", alpha = 0.3, size=0.1)),
#           upper = list(continuous = wrap("cor", size = 2))) +
#   theme(strip.placement = "outside", text = element_text(size = 5))

#ggplotly(p)
# pairs(subset, main = "test",
#       pch = 21, bg = c("red", "blue")[unclass(subset$Percent_Oxygen)])

# library(GGally)
# d <- highlight_key(iris)
# p <- ggpairs(d, aes(colour = Species), columns = 1:5)
# ggplotly(p) %>% 
#   highlight("plotly_selected")

df = read.table("https://raw.githubusercontent.com/clarewest/RFQAmodel/master/data/RFQAmodel_training.txt", 
    header = TRUE, stringsAsFactors = FALSE) %>% filter(Target %in% c("2OBA_B", "3HSB_D")) %>% 
    select(Target, EigenTHREADER, SAINT2, TMScore)

ggplot2::theme_set(ggplot2::theme_bw())  ## (this just globally sets the ggplot2 theme to theme_bw) 
df %>% select(-Target) %>% ggpairs()

df %>% ggpairs(., mapping = ggplot2::aes(colour = Target), lower = list(continuous = wrap("smooth", 
    alpha = 0.3, size = 0.1)))

# data1<- data_combined %>% select(Percent_Oxygen, Days_Grown, Telomere_Length)
# %>% group_by(Percent_Oxygen, Days_Grown)%>% summarise_at(c('Telomere_Length'),
# mean) %>% # summarise(TL = mean(Telomere_Length, na.rm = TRUE)) %>%
# spread(Percent_Oxygen, Telomere_Length) data2 <- data_combined %>%
# select(Percent_Oxygen, Days_Grown, ATPox, ATPglyc, cf_nDNA, cf_mtDNA) %>%
# group_by(Percent_Oxygen, Days_Grown)%>% summarise_at(c('Telomere_Length',
# 'ATPox'), mean) summarise(TL = mean(Telomere_Length, na.rm = TRUE)) %>%
# spread(Percent_Oxygen, TL, ATPox, ATPglyc, cf_nDNA, cf_mtDNA)



# , ATPox, ATPglyc, cf_nDNA, cf_mtDNA)

# df <- data_normoxia %>% select_if(is.numeric) %>% cor() %>% round(digits = 2)
# %>% as.data.frame() %>% filter_all(any_vars(abs(.) > 0.5)) df <- data_normoxia
# %>% select_if(is.numeric) %>% cor() %>% round(digits = 2) %>% as.data.frame()
# %>% select_if(funs(any(abs(.) > 0.5))) temp = cor(mtcars) temp[rowSums(temp >
# 0.4) > 0, colSums(temp > 0.4) > 0] df1 <- data_normoxia %>%
# select_if(is.numeric) %>% cor() %>% round(digits = 2) %>% as.data.frame() %>%
# rownames_to_column %>% gather(colname, value, -rowname) %>% filter(abs(value)
# >= 0.4)

# nvars <- colnames(data_hypoxia) # To make things easy, the variable names are
# changed into x1 to xn newvarnames <- paste( 'x',1:nvars, sep='' ) # Add the new
# variable names to the matrices dimnames( cormat1 ) <- dimnames( cormat2 ) <-
# list( newvarnames, newvarnames ) # Here's the model model <- paste( 'X',
# 1:nvars, ' =~ ', 'x', 1:nvars, ';', sep='' ) # Fit the model fit <- sem( model,
# sample.cov = list( cormat1, cormat2 ), sample.nobs = c( nobs1, nobs2 ),
# group.equal = 'lv.covariances' ) # This is the outcome of the (Chi^2) test
# lavTestLRT( fit )[ 2, 5:7 ] sqrt(M^2) A <- sqrt(M^2)-sqrt(P^2) Heatmap(A)
# corrplot(A, method = 'circle', tl.col = 'black', tl.cex=0.3, title='21-3%
# Oxygen') library(ggplot2) library(plotly) library(reshape) library(Hmisc)
# library(viridis) x <- data_normoxia y <- as.matrix(x) r <- rcorr(y) m1 <-
# reshape::melt(r$r) m2 <- reshape::melt(r$P) p.value <- m2$value p <- ggplot(m1,
# aes(X1, X2, fill = value, label=p.value)) + geom_tile() + labs(x='',y='') +
# scale_fill_viridis() p ggplotly(p) ### ## # library(plotly) # trace1 <- list( #
# type = 'heatmap', # x = nums_vars, # y = nums_vars, # z = M # ) # data <-
# list(trace1) # layout <- list(title = 'Features Correlation Matrix') # p <-
# plot_ly() # p <- add_trace(p, type=trace1$type, x=trace1$x, y=trace1$y,
# z=trace1$z) # p <- layout(p, title=layout$title) # p # Compute correlation
# coefficients cor.coef <- cor(df) # Compute correlation p-values cor.test.p <-
# function(x){ FUN <- function(x, y) cor.test(x, y)[['p.value']] z <- outer(
# colnames(x), colnames(x), Vectorize(function(i,j) FUN(x[,i], x[,j])) )
# dimnames(z) <- list(colnames(x), colnames(x)) z } p <- cor.test.p(test) #
# Create the heatmap heatmaply_cor( cor.coef, node_type = 'scatter',
# point_size_mat = -log10(p), point_size_name = 'p-value', label_names = c('x',
# 'y', 'Correlation') )

# index=sapply(iris, is.numeric) iris_numeric<-iris[index==TRUE] #p.mat <-
# cor.mtest(iris_numeric)$p p.mat <- corrr::correlate(iris_numeric)%>%as.matrix()
# iris_numeric%>% cor() %>% corrplot::corrplot( method = 'color',order =
# 'hclust',p.mat = p.mat, sig.level = 0.01 , pch.col = 'white',col = brewer.pal(n
# = 11, name = 'PuOr')) M <- cor(iris_numeric) corrplot.mixed(M,upper = 'color',
# tl.col = 'black') iris_numeric%>%cor()%>%corrplot::corrplot() library(ggplot2)
# library(plotly) library(reshape) library(Hmisc) library(viridis) x <-
# iris_numeric y <- as.matrix(x) r <- rcorr(y) m1 <- melt(r$r) m2 <- melt(r$P)
# p.value <- m2$value p <- ggplot(m1, aes(X1, X2, fill = value, label=p.value)) +
# geom_tile() + labs(x='',y='')+ scale_fill_viridis() ggplotly(p)

library(DT)
datatable(mtcars, rownames = FALSE, filter = "top", options = list(pageLength = 5, 
    scrollX = T))

mtcars[1:5, "mpg", drop = FALSE]

library(plotly)
set.seed(100)
d <- diamonds[sample(nrow(diamonds), 1000), ]
plot_ly(d, x = ~carat, y = ~price, color = ~carat, size = ~carat, text = ~paste("Clarity: ", 
    clarity))